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发表刊物：IEEE Transactions on Geoscience and Remote Sensing

关键字：Feature extraction , Radiometry , Recurrent neural networks , Sensors , Data mining , Remote sensing

摘要：With the rapid development of Earth observation technology, very-high-resolution (VHR) images from various satellite sensors are more available, which greatly enrich the data source of change detection (CD). Multisource multitemporal images can provide abundant information on observed landscapes with various physical and material views, and it is exigent to develop efficient techniques to utilize these multisource data for CD. In this article, we propose a novel and general deep siamese convolutional multiple-layers recurrent neural network (RNN) (SiamCRNN) for CD in multitemporal VHR images.

合写作者：Bo Du,Liangpei Zhang,Le Wang

论文类型：期刊论文

通讯作者：Chen Wu

文献类型：J

页面范围：2848 - 2864

ISSN号：0196-2892

是否译文：否

发表时间：2019-12-20

收录刊物：SCI

发布期刊链接：https://ieeexplore.ieee.org/document/8937755